Triple
T19051073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Odessa, Russian Empire |
E466257
|
entity |
| Predicate | hadSignificantLanguage |
P207
|
FINISHED |
| Object | Yiddish |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Yiddish | Statement: [Odessa, Russian Empire, hadSignificantLanguage, Yiddish]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadSignificantLanguage Context triple: [Odessa, Russian Empire, hadSignificantLanguage, Yiddish]
-
A.
hasSignificantLanguage
chosen
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
B.
hasStrongLanguage
Indicates that the subject contains or uses intense, offensive, or explicit language.
-
C.
hasLanguageEvidenceOf
Indicates that there is linguistic or textual evidence supporting, documenting, or attesting to the related entity or claim.
-
D.
hasContactWithLanguage
Indicates that an entity has some form of interaction, exposure, or engagement with a particular language.
-
E.
hasMajorityLanguageHistorically
Indicates that a particular language has historically been the predominant or majority language within a given entity or region.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8dd040fb881909af2a964f65ad208 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5dc02597c8190b39fd2c7b7e42258 |
completed | April 20, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69e4b99633c8819097988608c278ecf8 |
completed | April 19, 2026, 11:16 a.m. |
Created at: April 10, 2026, 12:03 p.m.